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AI uncovers hidden immune defenses inside bacteria

Researchers at the Massachusetts Institute of Technology (MIT) have discovered thousands of new proteins that protect bacteria from virus attacks using an AI system called DefensePredictor. What would usually take months of lab work can now be narrowed down to promising candidates in minutes.

Bacteria are under constant attack from viruses called bacteriophages. One of their most powerful defenses is CRISPR-Cas, a system that cuts up viral DNA to stop an infection and is now a valuable biotechnology tool for precisely editing genes in a lab.

Traditional methods of finding these defenses are long and laborious, equivalent to looking for a needle in a haystack. They involve searching for nearby known defensive genes and manually testing thousands of DNA fragments. But now, AI can take the strain.

AI trained like a Rubik’s Cube solver simplifies particle physics equations

For years, Rutgers physicist David Shih solved Rubik’s Cubes with his children, twisting the colorful squares until the scrambled puzzle returned to order. He didn’t expect the toy to connect to his research, but recently he realized the logic behind the puzzle was exactly what he needed to solve a problem involving particle physics.

That idea led to a new artificial intelligence (AI) method that can simplify some of the extremely complex equations used in particle physics. Shih described the method in a study posted to the arXiv preprint server, a widely used site where scientists share new research.

“In reaching our solutions, we found that an analogy between mathematical simplification and solving Rubik’s Cubes was key,” said Shih, a professor in the Department of Physics and Astronomy at the Rutgers School of Arts and Sciences. “Both can be viewed as scrambling and unscrambling problems.”

The Final Device

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For a century, the human-computer interface has been shrinking, and now, it’s crossing the final physiological barrier: your nervous system. In this episode of Technomics, we expose the terrifying and awe-inspiring evolutionary roadmap of the \.

AI models don’t only show evidence of ‘self-preservation.’ They will scheme to prevent other AIs from being shut down too, new research shows

Models from Anthropic, OpenAI, and Google will inflate performance reviews and exfiltrate model weights to prevent “peers” from being shut down.

Gene signature predicting lung cancer recurrence

Using gene activity measurements, the researcher found more than 400 genes that differ between tumors with and without vascular invasion and confirmed these patterns in an independent cohort. They then developed and validated a machine-learning predictor that predicts whether vascular invasion is present. They found this test worked well at predicting tumor recurrence in other datasets and, crucially, gave accurate results about vascular invasion when measured in tiny biopsy samples taken before surgery.

The researchers believe this predictor will play an important role in picking a treatment matched to how aggressive the tumor is.

According to the researchers, there is growing evidence that vascular invasion is associated with poor prognosis in other kinds of cancer, such as breast, liver and gastric cancer. The researchers need to determine if the same genes that are active in vascular-invasive lung adenocarcinoma are altered in other cancers. ScienceMission sciencenewshighlights.

Lung cancer is the leading cause of death from cancer. It kills more people in the U.S. than breast, prostate and colon cancer combined. When lung adenocarcinoma, the most common primary lung cancer in the U.S., grows into nearby blood vessels (a process called vascular invasion), the tumor is more likely to recur even if surgically removed. Pathologists can identify areas of vascular invasion post-operatively, but surgeons could perform more extensive surgery to lower the risk of recurrence if they could predict which tumors were more likely to have vascular invasion.

Researchers believe they have, for the first time, identified genes whose activity changes in lung tumors with vascular invasion. Additionally, they also discovered that they could detect these changes in small pieces of the tumor collected during a presurgical biopsy procedure.

“We think this is a potential game changer for patients with early-stage lung cancer,” says the corresponding author. “Our findings suggest a simple biopsy-based test could help doctors better identify patients at higher risk of recurrence and guide treatment decisions.”

Continue reading “Gene signature predicting lung cancer recurrence” | >

New AI video tool removes objects without breaking the laws of physics

When movie and TV directors want to tinker with their footage in post-production, they have an array of tools at their disposal to perfect a scene if it wasn’t shot exactly how they liked. That includes removing objects like stray equipment or unwanted background actors. But the tech has its limits when it comes to more complex physical interactions.

For example, if you want to remove an object that was bumping into or supporting something else, traditional tools often leave the remaining objects behaving in ways that defy the laws of physics, like a character hovering mid-air if the chair they were sitting on is deleted.

What this AI epitope library means for vaccines, immunotherapy and biosensors

A new tool makes it possible to screen millions of tiny protein fragments and select those that can be recognized by the immune system. The CIC biomaGUNE Center for Cooperative Research in Biomaterials has developed epiGPTope, a system that uses machine learning to generate and classify epitopes, in collaboration with the company Multiverse Computing.

The immune system is triggered by the presence of viruses or bacteria. When the antibodies produced recognize the epitopes, a small part of these viruses or bacteria, they launch an attack strategy. These epitopes are small fragments of protein recognized by antibodies or by immune cell receptors. So discovering new epitope sequences that target specific antibodies is essential for the development of diagnostic tools, immunotherapies and vaccines.

CIC biomaGUNE’s Biomolecular Nanotechnology laboratory, led by the Ikerbasque Research Professor Aitziber L. Cortajarena, is creating a library or database of hundreds of thousands of synthetic epitopes using this AI-based technique. The work is published in the journal ACS Synthetic Biology.

Metamaterial chains learn new shapes by sharing data hinge to hinge

In a new Nature Physics publication, University of Amsterdam researchers introduce human-made materials that spring to life. These ‘metamaterials’ don’t just learn to change shape, but can autonomously adapt their shape-changing strategy, perform reflex actions and move around like living systems do.

Normal materials have fixed, predetermined responses when a force is applied to them, whereas robots have pre-programmed behaviors. In stark contrast, living materials such as cells and brainless organisms can adapt extremely well to changing conditions. Inspired by nature, the research team created synthetic materials—metamaterials—that learn and adapt without a central “brain.”

The worm-like metamaterials progressively learn how to change shape by being trained on examples. They can forget old shapes and learn new ones, or learn and remember multiple shapes at once and toggle between these shapes. This allows them to perform advanced tasks such as grabbing an object or moving around (locomotion).

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